Production Data Automation Insights: A Python-Powered Journey from Outlook to PowerBI
In the dynamic world of petroleum engineering, the daily grind often involves wrestling with torrents of production data. As a practitioner in the field, I found myself immersed in the time-consuming task of manually handling daily production reports.
Manually extracting, processing, and managing this data not only consumed valuable time but also left room for human error. It was clear that a more efficient solution was needed to free up time for more strategic, value-added tasks and, most importantly, to elevate the accuracy of our insights.
This challenge sparked the inception of a project aimed at automating the entire production data pipeline — from the initial extraction of reports from Outlook to the visualization of key insights using PowerBI. The goal? To not just alleviate the burden of manual labor but to unlock a new level of efficiency and clarity in understanding our production landscape.
In the following sections, I’ll guide you through the solution that emerged from this challenge, blending petroleum engineering acumen with Python’s prowess.
Solution Overview:
Have you ever wished there was a way to make handling daily production reports a breeze? That’s where my solution comes in.
1. Data Extraction :
First off, we needed a way to pull data automatically from our daily production reports in Outlook. Enter Python, the wizard behind the scenes. With a few lines of code, we set up a system that grabs the latest reports without us lifting a finger.
2. Data Processing:
Now that we had the data, it was time to make sense of it. Python stepped up again, helping us process the information in a way that’s not only fast but also error-free. No more manual errors messing up our numbers.
3. Insert Into Database:
Instead of juggling files, we decided to store all this processed data in a database. This way, the data is neatly organized and ready for action whenever we need it. Think of it as a digital filing cabinet, but way cooler.
4. Data Visualization Using PowerBi:
The Last part is to visualize the data to gain some insights using a nice software like PowerBi for Visualizations like the production profile of each entity ( Filed, Reservoir, Well …etc ).
In a nutshell, this solution automates the boring stuff, leaving us more time to focus on what matters — understanding our production data without the headache.
Technical Overview:
Here is a brief about the python libraries I used :
1. win32com.client:
- Role: The hero behind Outlook extraction.
- Function: Seamlessly grabs those crucial daily production reports from Outlook, automating the manual retrieval process.
2. openpyxl:
- Role: The Excel wizard.
- Function: Dives into Excel files, extracts tables, and fetches vital data. Making sense of those intricate spreadsheets? Done.
3. pandas:
- Role: The data maestro.
- Function: Takes messy data and transforms it into a clean, organized format. From handling to processing, pandas do it with finesse.
4. shutil:
- Role: The mover and shaker.
- Function: Efficiently moves the extracted file to its designated location on the local PC. Keeping things tidy and accessible.
For the code you can have it from this github link:
you can use my simple code after removing some parts related to specific data into you workflow and modify only small parts of the code.
Results, Lessons, Call to Action, and Conclusion:
Lessons Learned:
- Every journey has its lessons. I finally applied a useful automation using python to my work which pleased me.
Call to Action:
- This is a very simple automation task and reducing the boring time-consuming stuff, future improvements are essential to make it better, handle more data, and be more robust.
Conclusion:
- Wrapping it up with a bow! This project wasn’t just about automating; it was about empowering and transforming into better workflow.
